The 5th Industrial Revolution (5IR) is reshaping the global business landscape by integrating artificial intelligence, robotics, and the Internet of Things with a renewed focus on human-centered innovation. Talent management (TM), traditionally regarded as a human resources function, must re-envision itself within this paradigm. This paper develops a conceptual framework that applies systems thinking and design thinking to talent management in the context of the 5IR, enabling organizations to remain agile, innovative, and resilient. Systems thinking offers a holistic perspective on understanding the interconnections within the talent ecosystem, while design thinking promotes creative, empathetic, and human-centered solutions. Drawing on recent research on coopetition in SMEs, project-based talent development, global talent practices, and digital readiness in the public sector, the framework highlights the importance of upskilling, leadership support, and the responsible adoption of AI. The outcomes suggest that organizations should adopt holistic and adaptive talent management practices to address skills gaps, foster innovation, and maintain a competitive advantage in the rapidly evolving global environment.
Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. We explore the application of MBSE to requirements engineering by extending the Model-Based Structured Requirement SysML Profile to comply with the INCOSE Guide to Writing Requirements while conforming to the ISO/IEC/IEEE 29148 standard requirement statement patterns. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition, verification & validation. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to assess its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support in the system architecture modeling software.
In this work, we describe the design and architecture of the open-source Quantum Engine Compiler (qe-compiler) currently used in production for IBM Quantum systems. The qe-compiler is built using LLVM's Multi-Level Intermediate Representation (MLIR) framework and includes definitions for several dialects to represent parameterized quantum computation at multiple levels of abstraction. The compiler also provides Python bindings and a diagnostic system. An open-source LALR lexer and parser built using Bison and Flex generates an Abstract Syntax Tree that is translated to a high-level MLIR dialect. An extensible hierarchical target system for modeling the heterogeneous nature of control systems at compilation time is included. Target-based and generic compilation passes are added using a pipeline interface to translate the input down to low-level intermediate representations (including LLVM IR) and can take advantage of LLVM backends and tooling to generate machine executable binaries. The qe-compiler is built to be extensible, maintainable, performant, and scalable to support the future of quantum computing.
Benjamin Decardi-Nelson, Abdulelah S. Alshehri, Akshay Ajagekar
et al.
This article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, general-purpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.
The evaporation heat transfer characteristics of R32 in an annular area outside a stainless steel sandblasted tube, dimple tube, dimple/sandblasted tube, and smooth tube with an outer diameter of 19.05 mm were investigated, and the effects of the mass flux [50–140 kg/(m2?s)], vapor quality (0.2–0.8), and saturation temperature (279–288 K) on the heat transfer coefficient were analyzed. The results show that the surface heat transfer coefficient is highest for the dimple/sandblasted tube, followed by the dimple tube, and lowest for the smooth tube. The surface heat transfer coefficient and frictional pressure drop are positively correlated with the mass flux and negatively correlated with the saturation temperature. The average vapor quality at high mass flux has a significant effect on the surface heat transfer coefficient. The enhanced heat transfer effect was quantified by introducing the enhancement factor ηEF and the performance evaluation factor ηPEF. The dimple/sandblasted tube combined the advantages of sandblasted and dimple surfaces, thus exhibiting the best evaporation heat transfer performance with the highest ηEF and ηPEF values of 2.84 and 2.31, respectively. The composite treatment of sandblasted and dimple heat transfer tube surfaces increases the heat transfer area and the number of vaporization cores, improving the turbulence intensity, such that the liquid film is stretched and thinned to promote evaporation outside the tube.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
In this paper, we identify and characterize the emerging area of representation engineering (RepE), an approach to enhancing the transparency of AI systems that draws on insights from cognitive neuroscience. RepE places population-level representations, rather than neurons or circuits, at the center of analysis, equipping us with novel methods for monitoring and manipulating high-level cognitive phenomena in deep neural networks (DNNs). We provide baselines and an initial analysis of RepE techniques, showing that they offer simple yet effective solutions for improving our understanding and control of large language models. We showcase how these methods can provide traction on a wide range of safety-relevant problems, including honesty, harmlessness, power-seeking, and more, demonstrating the promise of top-down transparency research. We hope that this work catalyzes further exploration of RepE and fosters advancements in the transparency and safety of AI systems.
Physics-Informed machine learning models have recently emerged with some interesting and unique features that can be applied to reservoir engineering. In particular, physics-informed neural networks (PINN) leverage the fact that neural networks are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations. The transient diffusivity equation is a fundamental equation in reservoir engineering and the general solution to this equation forms the basis for Pressure Transient Analysis (PTA). The diffusivity equation is derived by combining three physical principles, the continuity equation, Darcy's equation, and the equation of state for a slightly compressible liquid. Obtaining general solutions to this equation is imperative to understand flow regimes in porous media. Analytical solutions of the transient diffusivity equation are usually hard to obtain due to the stiff nature of the equation caused by the steep gradients of the pressure near the well. In this work we apply physics-informed neural networks to the one and two dimensional diffusivity equation and demonstrate that decomposing the space domain into very few subdomains can overcome the stiffness problem of the equation. Additionally, we demonstrate that the inverse capabilities of PINNs can estimate missing physics such as permeability and distance from sealing boundary similar to buildup tests without shutting in the well.
Fabio Calefato, Luigi Quaranta, Filippo Lanubile
et al.
Background. Due to the widespread adoption of Artificial Intelligence (AI) and Machine Learning (ML) for building software applications, companies are struggling to recruit employees with a deep understanding of such technologies. In this scenario, AutoML is soaring as a promising solution to fill the AI/ML skills gap since it promises to automate the building of end-to-end AI/ML pipelines that would normally be engineered by specialized team members. Aims. Despite the growing interest and high expectations, there is a dearth of information about the extent to which AutoML is currently adopted by teams developing AI/ML-enabled systems and how it is perceived by practitioners and researchers. Method. To fill these gaps, in this paper, we present a mixed-method study comprising a benchmark of 12 end-to-end AutoML tools on two SE datasets and a user survey with follow-up interviews to further our understanding of AutoML adoption and perception. Results. We found that AutoML solutions can generate models that outperform those trained and optimized by researchers to perform classification tasks in the SE domain. Also, our findings show that the currently available AutoML solutions do not live up to their names as they do not equally support automation across the stages of the ML development workflow and for all the team members. Conclusions. We derive insights to inform the SE research community on how AutoML can facilitate their activities and tool builders on how to design the next generation of AutoML technologies.
Configurable software systems can be tuned for better performance. Leveraging on some Pareto optimizers, recent work has shifted from tuning for a single, time-related performance objective to two intrinsically different objectives that assess distinct performance aspects of the system, each with varying aspirations. Before we design better optimizers, a crucial engineering decision to make therein is how to handle the performance requirements with clear aspirations in the tuning process. For this, the community takes two alternative optimization models: either quantifying and incorporating the aspirations into the search objectives that guide the tuning, or not considering the aspirations during the search but purely using them in the later decision-making process only. However, despite being a crucial decision that determines how an optimizer can be designed and tailored, there is a rather limited understanding of which optimization model should be chosen under what particular circumstance, and why. In this paper, we seek to close this gap. Firstly, we do that through a review of over 426 papers in the literature and 14 real-world requirements datasets. Drawing on these, we then conduct a comprehensive empirical study that covers 15 combinations of the state-of-the-art performance requirement patterns, four types of aspiration space, three Pareto optimizers, and eight real-world systems/environments, leading to 1,296 cases of investigation. We found that (1) the realism of aspirations is the key factor that determines whether they should be used to guide the tuning; (2) the given patterns and the position of the realistic aspirations in the objective landscape are less important for the choice, but they do matter to the extents of improvement; (3) the available tuning budget can also influence the choice for unrealistic aspirations but it is insignificant under realistic ones.
Today's production systems are complex networks of cyber-physical systems which combine mechanical and electronic parts with software and networking capabilities. To the inherent complexity of such systems additional complexity arises from the context in which these systems operate. Manufacturing companies need to be able to adapt their production to ever changing customer demands as well as decreasing lot sizes. Engineering such systems, which need to be combined and reconfigured into different networks under changing conditions, requires engineering methods to carefully design them for possible future uses. Such engineering methods need to preserve the flexibility of functions into runtime, so that reconfiguring machines can be done with as little effort as possible. In this paper we present a model-based approach that is focused on machine functions and allows to methodically develop system functionalities for changing system networks. These functions are implemented as so-called skills using automated code-generation.
Gopi Krishnan Rajbahadur, Shaowei Wang, Yasutaka Kamei
et al.
Researchers usually discretize a continuous dependent variable into two target classes by introducing an artificial discretization threshold (e.g., median). However, such discretization may introduce noise (i.e., discretization noise) due to ambiguous class loyalty of data points that are close to the artificial threshold. Previous studies do not provide a clear directive on the impact of discretization noise on the classifiers and how to handle such noise. In this paper, we propose a framework to help researchers and practitioners systematically estimate the impact of discretization noise on classifiers in terms of its impact on various performance measures and the interpretation of classifiers. Through a case study of 7 software engineering datasets, we find that: 1) discretization noise affects the different performance measures of a classifier differently for different datasets; 2) Though the interpretation of the classifiers are impacted by the discretization noise on the whole, the top 3 most important features are not affected by the discretization noise. Therefore, we suggest that practitioners and researchers use our framework to understand the impact of discretization noise on the performance of their built classifiers and estimate the exact amount of discretization noise to be discarded from the dataset to avoid the negative impact of such noise.
The order/dimension of models derived on the basis of data is commonly restricted by the number of observations, or in the context of monitored systems, sensing nodes. This is particularly true for structural systems (e.g., civil or mechanical structures), which are typically high-dimensional in nature. In the scope of physics-informed machine learning, this paper proposes a framework -- termed Neural Modal ODEs -- to integrate physics-based modeling with deep learning for modeling the dynamics of monitored and high-dimensional engineered systems. Neural Ordinary Differential Equations -- Neural ODEs are exploited as the deep learning operator. In this initiating exploration, we restrict ourselves to linear or mildly nonlinear systems. We propose an architecture that couples a dynamic version of variational autoencoders with physics-informed Neural ODEs (Pi-Neural ODEs). An encoder, as a part of the autoencoder, learns the abstract mappings from the first few items of observational data to the initial values of the latent variables, which drive the learning of embedded dynamics via physics-informed Neural ODEs, imposing a modal model structure on that latent space. The decoder of the proposed model adopts the eigenmodes derived from an eigen-analysis applied to the linearized portion of a physics-based model: a process implicitly carrying the spatial relationship between degrees-of-freedom (DOFs). The framework is validated on a numerical example, and an experimental dataset of a scaled cable-stayed bridge, where the learned hybrid model is shown to outperform a purely physics-based approach to modeling. We further show the functionality of the proposed scheme within the context of virtual sensing, i.e., the recovery of generalized response quantities in unmeasured DOFs from spatially sparse data.
David J. Whitworth, Rowan J. Smith, Ralf S. Klessen
et al.
Many studies concluded that magnetic fields suppress star formation in molecular clouds and Milky Way like galaxies. However, most of these studies are based on fully developed fields that have reached the saturation level, with little work on investigating how an initial weak primordial field affects star formation in low metallicity environments. In this paper, we investigate the impact of a weak initial field on low metallicity dwarf galaxies. We perform high-resolution AREPO simulations of five isolated dwarf galaxies. Two models are hydrodynamical, two start with a primordial magnetic field of 10$^{-6} μ$G and different sub-solar metallicities, and one starts with a saturated field of 10$^{-2} μ$G. All models include a non-equilibrium, time-dependent chemical network that includes the effects of gas shielding from the ambient ultraviolet field. Sink particles form directly from the gravitational collapse of gas and are treated as star-forming clumps that can accrete gas. We vary the ambient uniform far ultraviolet field, and cosmic ray ionization rate between 1\% and 10\% of solar values. We find that the magnetic field has little impact on the global star formation rate, which is in tension with some previously published results. We further find that the initial field strength has little impact on the global star formation rate. We show that an increase in the mass fractions of both molecular hydrogen and cold gas, along with changes in the perpendicular gas velocity dispersion and the magnetic field acting in the weak-field model, overcome the expected suppression in star formation.
The airside mass transfer characteristics of two-plate fin-and-tube heat exchangers with different fin spacings were experimentally studied under low ambient pressure and dehumidifying conditions. The experimental conditions comprised an ambient pressure range 40–100 kPa, inlet air velocity 0.5–4 m/s, inlet air relative humidity 50%–90%, inlet air dry bulb temperature 27 ℃, and water flow velocity 1.65 m/s. The effects of ambient pressure, air velocity, fin spacing, and relative humidity of the inlet air on the airside mass transfer characteristics of the heat exchanger were investigated. The results showed that under low ambient pressure, the effects of face air velocity, fin spacing, and inlet air relative humidity on the air side mass transfer coefficient of the heat exchanger is consistent with that under normal ambient pressure. When the ambient pressure increased from 40 kPa to 100 kPa, the air-side mass transfer coefficient decreased by 18.2%–23.6%. Under low air velocity and ambient pressure, the effects of fin spacing and inlet air relative humidity on the air-side mass transfer characteristics are more significant.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
Software project management makes extensive use of predictive modeling to estimate product size, defect proneness and development effort. Although uncertainty is acknowledged in these tasks, fuzzy inference systems, designed to cope well with uncertainty, have received only limited attention in the software engineering domain. In this study we empirically investigate the impact of two choices on the predictive accuracy of generated fuzzy inference systems when applied to a software engineering data set: sampling of observations for training and testing; and the size of the rule set generated using fuzzy c-means clustering. Over ten samples we found no consistent pattern of predictive performance given certain rule set size. We did find, however, that a rule set compiled from multiple samples generally resulted in more accurate predictions than single sample rule sets. More generally, the results provide further evidence of the sensitivity of empirical analysis outcomes to specific model-building decisions.
Konstantina Dritsa, Thodoris Sotiropoulos, Haris Skarpetis
et al.
How different are search engines? The search engine wars are a favorite topic of on-line analysts, as two of the biggest companies in the world, Google and Microsoft, battle for prevalence of the web search space. Differences in search engine popularity can be explained by their effectiveness or other factors, such as familiarity with the most popular first engine, peer imitation, or force of habit. In this work we present a thorough analysis of the affinity of the two major search engines, Google and Bing, along with DuckDuckGo, which goes to great lengths to emphasize its privacy-friendly credentials. To do so, we collected search results using a comprehensive set of 300 unique queries for two time periods in 2016 and 2019, and developed a new similarity metric that leverages both the content and the ranking of search responses. We evaluated the characteristics of the metric against other metrics and approaches that have been proposed in the literature, and used it to (1) investigate the similarities of search engine results, (2) the evolution of their affinity over time, (3) what aspects of the results influence similarity, and (4) how the metric differs over different kinds of search services. We found that Google stands apart, but Bing and DuckDuckGo are largely indistinguishable from each other.
In this study, a cabinet semiconductor cooling device was designed and its refrigerating capacity and cooling efficiency were tested using a test platform. The performance parameters were obtained according to test-based analyses of the refrigerating capacity, cooling efficiency, and other factors. According to the test results, for an average indoor temperature of 26 ℃ and 200-360 W input power, the cooling efficiency first increases and then decreases, reaching a maximum value of 69.5% when the input power is 232 W. Meanwhile, the average amount of refrigeration throughout the test is 455.46 kJ and the power of the semiconductor cooling device is 151.8 W, which can remove 3.04% of the energy in a 5 kW communication cabinet.
Heating and ventilation. Air conditioning, Low temperature engineering. Cryogenic engineering. Refrigeration
We propose a three terminal heat engine based on semiconductor superlattices for energy harvesting. The periodicity of the superlattice structure creates an energy miniband, giving an energy window for allowed electron transport. We find that this device delivers a large power, nearly twice than the heat engine based on quantum wells, with a small reduction of efficiency. This engine also works as a refrigerator in a different regime of the system's parameters. The thermoelectric performance of the refrigerator is analyzed, including the cooling power and coefficient of performance in the optimized condition. We also calculate phonon heat current through the system, and explore the reduction of phonon heat current compared to the bulk material. The direct phonon heat current is negligible at low temperatures, but dominates over the electronic at room temperature and we discuss ways to reduce it.